CN115062811B - Optimizing method and system for new energy planning scheme integrating economic factors and energy factors - Google Patents
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Abstract
An optimization method and system for a new energy planning scheme integrating economic factors and energy factors relates to the technical field of power system optimization. The problem that the optimization result cannot be considered with all indexes due to single consideration factors of the existing optimization strategy is solved. The method of the invention comprises the following steps: and (5) electricity load prediction: predicting the electricity load in the project effective period by using a time sequence and a machine learning mutual auxiliary method to obtain an electricity load predicted value; nonlinear optimization: according to the electricity load predicted value, nonlinear optimization processing is carried out by using a plurality of constraint conditions, and an optimal charging scheme, optimal power and an optimal position of the energy system are obtained; economics assessment: according to the nonlinear optimization result, economic evaluation is carried out by combining economic parameters and risk avoidance probability; and repeating the nonlinear optimization and the economics evaluation steps to perform iterative processing until the maximum NVP expected value is obtained, and completing optimization. The method is suitable for the technical field of optimizing new energy construction schemes.
Description
Technical Field
The invention belongs to a power system, and particularly relates to the technical field of optimization of the power system.
Background
In technical reports and papers related to existing energy storage systems, attention is generally focused on discussing the good place and market potential of energy storage of a power grid, and on two applications of the energy storage system, namely power grid balance and energy arbitrage. In the prior art, the related literature discloses mostly the interactions between electric vehicles and the power grid and the methods of deploying plug-in electric vehicles (PEVs) as energy storage and their charging topologies and infrastructures.
For example: patent document CN109787259a discloses a multi-type energy storage joint planning method based on random fluctuation of new energy, and proposes that the multi-type energy storage joint planning method has better economical efficiency and energy storage utilization efficiency compared with a single-type energy storage planning method, and can better meet various requirements of system operation, thereby greatly improving the level of new energy consumption. The scheme realizes the joint planning of multiple types of energy storage systems from the angles of economy and energy utilization rate.
For example, patent document CN109492815a discloses a method for optimizing the site selection and the volume determination of an energy storage power station facing a power grid under a market mechanism, and the disclosed technical scheme is that from the energy storage facing the power grid, the cost benefits of the energy storage participating in the electric power market are firstly analyzed from two aspects of direct benefit and indirect benefit, on the basis, a multi-target double-layer planning model for optimizing and configuring the energy storage comprehensively considering planning and operation is established, and the site selection and the volume determination of the energy storage are realized.
For example, patent document CN112821397B discloses a low-carbon economic dispatching method and device with coordinated source-load-storage, the invention calculates a load at a certain moment according to a price type demand response model and an excitation type demand response model, and inputs the obtained load into an upper layer optimized dispatching model considering reasonable energy abandoning and variable working condition characteristics of energy storage equipment, so that residual load and energy storage charge-discharge power in a certain period are obtained, and the effect that reasonable energy abandoning at the source side and multi-type demand response at the load side are introduced into a power system for dispatching is achieved, so that the economy and the accuracy of calculation of the low-carbon economic dispatching model can be effectively improved.
In summary, existing optimization strategies mostly consider a single factor, such as: the optimization strategy is considered only from the economical and utilization angles of the energy storage system, the optimization strategy is considered only from the site selection factors and the economic benefit maximization angles, or the optimization strategy is considered only from the economic factors such as price and the like. Because the optimization strategy considers single factors, the planning result can only meet one or two indexes, and cannot meet all indexes, along with the development of society, the requirements on an energy system are higher and higher, and the planning method only considering single factors is not suitable for the social requirement.
As is known, with the reform of a new power system, the construction of the power market is steadily and orderly advanced, the pattern of a multi-competition main body is initially formed, and the effect of the market in resource optimization configuration is obviously enhanced. And economic factors such as site selection, price and the like can effectively optimize the power resource allocation. Through the optimization algorithm, the battery storage system can help the power grid to assist in allocating the generator set according to the operation and stability of the generator set and the electricity consumption amount, so that the average power generation cost and the power price in the area are effectively reduced. Meanwhile, the problem of blockage in the high-voltage transmission line area can be effectively solved through site selection, so that resource optimization configuration is effectively carried out.
Disclosure of Invention
The invention solves the problems that the existing optimization strategy has single consideration factor, so that the optimization result cannot consider all indexes and is not suitable for implementation.
The scheme provided by the invention is as follows:
an optimization method of a new energy planning scheme integrating economic factors and energy factors, comprising the following steps:
step 1, electricity load prediction: predicting the electricity load in the project effective period by using a time sequence and a machine learning mutual auxiliary method to obtain an electricity load predicted value;
step 2, nonlinear optimization: according to the electricity load predicted value, nonlinear optimization processing is carried out by using a plurality of constraint conditions, and an optimal charging scheme, optimal power and an optimal position of the energy system are obtained;
step 3, economic evaluation: according to the nonlinear optimization result, carrying out economic evaluation by combining economic parameters and risk avoidance probability, and then obtaining an NVP expected value;
and (3) taking the obtained NVP expected value as an input condition of the nonlinear optimization step, returning to the execution step 2, and then circularly executing the steps 2 and 3 to perform iterative processing until the maximum NVP expected value is obtained, wherein the optimal charging scheme, the optimal power and the optimal position of the optimal energy system corresponding to the maximum NVP expected value are taken as optimization results.
Preferably, in step 3, the economic evaluation, including the evaluation of the electric power spot market price, is implemented by modeling, the process is based on the construction of an improved GARCH model:
wherein ω, α and β are parameters of the GARCH model calculated using the maximum likelihood method, r t Represents the logarithmic rate of return, sigma, of the power arbitrage at time t t A fluctuation rate of the electric power price at time t is represented;
then calculating to obtain the predicted standard deviation by using the electricity price return and the standard deviation in the previous time period, P T The node electricity price of the current-stage electric power is:
epsilon represents the rate of rise of the price of electricity between time periods 0 and T,the Brownian motion progress is represented, wherein E is inverse normal distribution, the mean value is 0, and the standard deviation is 1-N (0, 1).
Further, in the nonlinear optimization, the designed objective function is:
minimization (F) = (capital cost + operating fixed cost + operating variable cost-energy arbitrage revenue).
Further, the constraints include rated energy of the energy storage system.
Further, the constraints further include a location marginal pricing constraint LMP:
LMP = system reference bus marginal price-system loss component-congestion component.
The method of the invention can be realized by adopting computer software, so that the invention also comprises an optimizing system of a new energy planning scheme for synthesizing economic factors and energy factors, which corresponds to the method, and the system comprises:
an electricity load prediction unit: the method is used for predicting the electricity load in the project effective period by using a time sequence and a machine learning mutual auxiliary method to obtain an electricity load predicted value;
nonlinear optimization unit: the method comprises the steps of carrying out nonlinear optimization processing by using a plurality of constraint conditions according to an electricity load predicted value to obtain an optimal charging scheme, optimal power and an optimal position of an energy system;
economics evaluation unit: the method comprises the steps of carrying out economic evaluation according to nonlinear optimization results and combining economic parameters and risk probability, and then obtaining an NVP expected value;
iteration unit: and the nonlinear optimization unit and the economic evaluation unit are started to perform iterative processing until the maximum NVP expected value is obtained, and the optimal charging scheme, the optimal power and the optimal position of the optimal energy system corresponding to the maximum NVP expected value are used as optimization results.
The method of the invention can be realized by adopting computer software, so that the invention also correspondingly protects a computer storage medium for storing a computer program, and when the storage medium is run by a computer, the method for optimizing any new energy planning scheme of the invention is executed.
The invention also protects a computer device which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the optimization method of any new energy planning scheme is executed.
Compared with the prior art, the invention simultaneously considers the following factors in planning:
1) New energy, cost, loss and other economic factors of the battery storage system;
2) Predicting a load, wind energy and solar power station by a machine learning method;
3) The new energy and the battery storage system are optimally selected in the power grid through optimization, so that the economic benefit is maximized;
4) The analysis of the electric power market transaction mainly comprises medium-and-long-term electric power transaction and electric power spot transaction, and auxiliary services such as frequency modulation, peak shaving, standby and the like are provided for the power grid through battery optimization charge and discharge, and the cooperative action of the auxiliary services and the new energy power generation station on the power grid is considered.
The invention scientifically and reasonably considers factors affecting various aspects of the energy storage system, integrates knowledge in a plurality of disciplinary fields such as electrical engineering, economics, energy science, chemistry and the like to optimize the planning scheme of new energy, so that the optimization scheme obtained by adopting the method can improve the economic benefit and reduce the investment risk of investors while improving the energy utilization rate.
In the technical field of new energy construction, an optimized planning scheme of energy construction is obtained with the aid of the method, and a battery energy storage system in the optimized scheme can greatly avoid risks in the running process of new energy and realize benefit maximization, so that the method plays a guiding role in investment construction of new energy projects.
The method of the invention is more beneficial to reasonably planning and developing new energy development of solar energy, wind energy and tidal energy, and the method of the invention can more attract investors to invest in implementation due to the considered economic factors, attract investment construction, accelerate the development and utilization of new energy, and achieve the effects of optimizing the energy utilization structure of the region and improving the ecological environment.
The optimization scheme obtained by the method of the invention is used for constructing a battery energy storage system, provides a new thought for solving the energy crisis and developing, constructing and popularizing new energy, and can effectively utilize the battery to optimize charge and discharge to provide auxiliary services such as frequency modulation, peak shaving, standby and the like for the power grid so as to improve the stability of the power grid.
The method is suitable for the technical field of optimizing new energy construction schemes.
Drawings
FIG. 1 is a data processing flow chart of the new energy optimization planning and economic prediction method of the invention.
Fig. 2 is an optimal charge-discharge scheme of the energy storage battery according to the seventh embodiment.
Detailed Description
Referring to fig. 1, a new energy optimizing and planning method according to the present embodiment is described, where the planning method includes:
step 1, electricity load prediction: predicting the electricity load in the project effective period by using a time sequence and a machine learning mutual auxiliary method to obtain an electricity load predicted value;
in this step, historical electricity load data of the ISO/RTO grid can be used as base data for prediction.
Step 2, nonlinear optimization: according to the electricity load predicted value, nonlinear optimization processing is carried out by using a plurality of constraint conditions, and an optimal charging scheme, optimal power and an optimal position of the energy system are obtained;
the above process can be realized by adopting the existing nonlinear optimization method.
The multiple constraints may be designed according to practical situations and may generally include: grid market data, zone boundary pricing reference values, admittance matrix of the grid, generator input power, wind energy, solar power stations and battery energy storage system parameters. The optimal charging scheme, the optimal power of each power generation station and the energy storage system and the corresponding optimal geographic position are obtained according to the parameters.
Step 3, economic evaluation: according to the nonlinear optimization result and the random input NVP expected value, carrying out economic evaluation by combining the economic parameter and the risk avoidance probability to obtain the NVP expected value;
and (3) taking the obtained economic evaluation result and the NVP expected value as one input condition of the nonlinear optimization step, returning to the execution step 2, and then circularly executing the steps 2 and 3 to perform iterative processing until the maximum NVP expected value is obtained, and taking the optimal charging scheme, the optimal power and the optimal position of the optimal energy system corresponding to the maximum NVP expected value as the optimization result.
The NVP refers to a net present value.
The economic parameter refers to an economic parameter related to a new energy power supply system, for example: there are no factors such as risk utilization rate, upgrade rate tax rate, discount mode, etc., risk avoidance rate entered by investors, and WACC (weighted average capital cost), etc. These factors are to evaluate the economic benefit and investment risk of the new energy system.
The expected value of the net present value is calculated in a random manner, and the proposed monte carlo risk analysis algorithm is able to randomly generate the relevant parameters from a predetermined distribution using monte carlo simulation and a kowski decomposition.
In this embodiment, the method for obtaining the NVP expected value is to extract the expected value in a random manner to calculate, and then to use a Cholesky decomposition (Cholesky) technique to decompose the correlation matrix of different parameters, and then to use Monte Carlo simulation (Monte Carlo) to generate the associated random parameters. These parameters are then used to derive the distribution of NPV.
In the step 3 of economic evaluation, parameters of external data wind energy, solar power stations and a battery energy storage system are required, information related to a risk-free interest rate, an upgrade rate tax rate and a discount mode related to economics is required to be input, investors are required to input information related to a risk avoidance rate and WACC, and economic evaluation is performed according to the novel data, so that a net present value expected value is obtained. The optimization scheme obtained according to the input parameters is made on the premise of comprehensively considering macroscopic and microscopic economic fluctuation factors, so that the obtained optimization scheme is closer to the actual situation, and can effectively help investors to avoid investment risks.
In the above process, iteration is performed by returning to the step 2, and the next feasible optimization scheme, the next optimal charging scheme of the energy system, the power and the access position are obtained.
And (3) obtaining various optimization schemes through multiple iterations, and then obtaining the optimal scheme by optimizing.
The method comprises the steps of firstly collecting historical data including factors such as risk-free interest rate, upgrading rate tax rate, discount mode and the like, and parameters such as risk avoidance rate input by investors, WACC (weighted average capital cost) and the like. The collected historical data is then integrated into a different distribution. A matrix of correlation coefficients between the different distributions is then calculated. Next, a Kelvin decomposition method is used to extract the Kelvin matrix. Then, the Monte Carlo method is used to randomly extract parameters from the matrix of calendar history data as a vector matrix. The vector matrix of the new interrelated random parameters is then taken by speaking this vector matrix and multiplying it by the kohls base matrix.
In the second embodiment, the new energy optimization planning method according to the first embodiment is further described, and in the step 3, in the economic evaluation, the evaluation method includes that the power spot market price is evaluated, the evaluation method is implemented through modeling, and the method for modeling the power spot market price is that the power price fluctuation rate is obtained according to the calculation of building an improved GARCH model:
wherein ω, α and β are parameters of the GARCH model calculated using the maximum likelihood method, r t The logarithmic yield of power arbitrage at time t is expressed as: represents electricity price ln (P) t /P t-1 ) Log-return at time t; sigma (sigma) t A fluctuation rate of the electric power price at time t is represented;
then calculating to obtain the predicted standard deviation by using the electricity price return and the standard deviation in the previous time period, P T The node electricity price of the current-stage electric power is:
epsilon represents the rate of rise of the price of electricity between time periods 0 and T,the Brownian motion progress is represented, wherein E is inverse normal distribution, the mean value is 0, and the standard deviation is 1-N (0, 1). T is the calculation period, for example: it may be 8760 hours a year.
In practice, the power spot price exhibits excessive volatility and there is a variance in both unconditional and conditional variance, which results in the inability to accurately reflect future spot market price conditions using the existing typical GARCH model using the constant σ. In order to make up for the defect of random walk of a drift model, the embodiment adopts an improved GARCH model to predict the fluctuation of the future electricity price, and the prediction result is more accurate than the original method.
In the third embodiment, the new energy optimization planning method described in the first embodiment is further described, and step 2 is illustrated in the present embodiment, and in the nonlinear optimization, the objective function of the design is:
minimization (F) = (capital cost + operating fixed cost + operating variable cost-energy arbitrage revenue).
The objective function of the existing nonlinear optimization is to use the maximization of the energy benefit minus the total cost, which is equivalent to the minimization of the total cost minus the energy benefit. The objective function described in this embodiment is a decreasing number before the original objective function, because the objective equation is a problem convex function to minimize in order to find the optimal solution.
The objective function described in this embodiment calculates the daily profit (24 hours) of the N battery storage systems by summing the discrete optimization functions, which have a time step of one hour. Specifically, the objective function may be written as:
wherein the method comprises the steps ofFor capital cost->For operation and maintenance, fix cost->For the operation and maintenance of variable costs, < >>Charging power for the h hour, i energy storage system, +.>For the h hour, the ith energy source stores the discharge power of the system. LMP i,h The node electricity price of the ith energy storage system in the h hour; ESS represents Energy Storage Systems as an energy storage system.
In the fourth embodiment, the new energy optimization planning method described in the first embodiment is further described, and the constraint condition described in the step 2 is exemplified by the present embodiment, where the constraint condition includes rated energy of the energy storage system.
The specific constraint conditions are as follows:
wherein the method comprises the steps ofFor the rated power of the battery storage system,
η + for charging efficiency, eta - In order for the discharge efficiency to be high,
E i,h for the h hour, the i energy storage system,
E i,h1 for the h-1 h, the i energy storage system,
the rated energy of the system is stored for the ith battery.
In the fifth embodiment, the new energy optimization planning method described in the first embodiment is further described, the step 2 is illustrated in the present embodiment, and the constraint conditions in the present embodiment include power parameters of a power plant, specifically:
in the formula, P represents active power, Q represents reactive power, subscript i and j represent the sequence number of the node, subscript h represents the h hour, subscript g represents the sequence number of the power station, and d represents the sequence number of the load, specifically:
P gi，h for the h hour, the active power of the g-th power station in the i-th node in the power grid,
P di,h for the h hour the power-consuming electric power of the d-th load in the i-th node in the grid,
P gi,min the minimum value of active power generated by the g-th power station of the ith node in the power grid,
P gi,max the maximum value of active power generated by the g-th power station of the ith node in the power grid,
Q gi,h reactive power for the g-th power station of the i-th node in the power grid at h-th hour,
Q gi,min the minimum value of reactive power generation power of the g-th power station for the i-th node in the power grid,
Q gi,max the maximum value of reactive power generation power of the g-th power station for the i-th node in the power grid,
Q di,h for the h hour the reactive power consumption of the d load in the i-th node in the grid,
V ,h for the h hour the voltage of the i-th node in the grid,
V min for the h hour the minimum value of the voltage of the i-th node in the grid,
V max for the h hour the maximum value of the voltage of the i-th node in the grid,
δ i,h for the h hour the angle of the i-th node in the grid,
G ij for the conductivity between the i-th node and the j-th node in the grid,
B ij is the susceptance between the ith node and the jth node in the power grid.
And the g-th power station of the i-th node in the power grid has a wired corresponding relation with the node. There may be multiple power stations for a node in the grid, but there are also nodes with only loads and no power stations.
In the sixth embodiment, the new energy optimization planning method described in the first embodiment is further described, and the constraint conditions described in the step 2 are exemplified by the constraint conditions described in the present embodiment, where the constraint conditions include a location marginal pricing constraint condition LMP:
LMP = system reference bus marginal price-system loss component-congestion component.
Location Marginal Pricing (LMP) as described in this embodiment is a mechanism that includes market-based prices to manage transmission congestion. The system additionally provides a marginal increase in cost of one megawatt LMP on the load bus k Expressed as:
where NLL denotes the number of lines of high voltage electrical connection at load limit,
is a line->A lagrangian multiplier of (c);
LMP ref the marginal bus price is referred to by the system and can be obtained through calculation in the formulas (1) and (2).
Is a line->The sensitivity of the line flow on to one megawatt change at bus k is used to make adjustments to the bus to keep the system load and generator balanced.
The power loss of the transmission line, which is generated by increasing the load flow of one megawatt on the transmission line, is represented as a line loss sensitivity coefficient, and the coefficient is as follows:
in the formula, the unit of resistance is ohm per kilometer, the unit of line length is kilometer, the unit of power is megawatt, and the unit of Voltage is kilovolt.
The seventh embodiment is an optimal charging scheme obtained by illustrating the new energy optimization planning method of the present invention:
referring to fig. 2, an optimal charging scheme obtained by the method of the present invention is shown, wherein the optimal charging scheme is shown for the energy storage system at 150 megawatts and for 2 hours in duration, the darker bars extending downward from the right side represent the discharge power, the lighter bars extending upward from the left side represent the charge power, the curve SOC with an "X" mark represents the state of charge of the battery, and the other curve Electricity Price represents the predicted real-time electricity price of the node electricity market. According to the graph, the energy storage system can be charged when the real-time electricity price of the node electricity market is low, and is discharged when the price is high, so that the benefits are realized and the economic utility is maximized.
Claims (5)
1. An optimization method of a new energy planning scheme integrating economic factors and energy factors is characterized by comprising the following steps:
step 1, electricity load prediction: predicting the electricity load in the project effective period by using a time sequence and a machine learning mutual auxiliary method to obtain an electricity load predicted value;
step 2, nonlinear optimization: according to the electricity load predicted value, nonlinear optimization processing is carried out by using a plurality of constraint conditions, and an optimal charging scheme, optimal power and an optimal position of the energy system are obtained;
step 3, economic evaluation: according to the nonlinear optimization result, carrying out economic evaluation by combining economic parameters and risk avoidance probability, and then obtaining an NVP expected value;
taking the obtained NVP expected value as an input condition of a nonlinear optimization step, returning to the execution step 2, and then circularly executing the steps 2 and 3 to perform iterative processing until the maximum NVP expected value is obtained, wherein the optimal charging scheme, the optimal power and the optimal position of the optimal energy system corresponding to the maximum NVP expected value are taken as optimization results;
the plurality of constraints described in step 2 include: rated energy of the energy storage system, power parameters of the power plant and location marginal pricing constraints LMP, wherein:
the rated energy of the energy storage system is specifically as follows:
wherein the method comprises the steps ofStoring rated power for a system of batteries，
η + For charging efficiency, eta - In order for the discharge efficiency to be high,
E i,h for the h hour, the i energy storage system,
E i,h-1 for the h-1 h, the i energy storage system,
storing rated energy of the system for an ith battery;
the power parameters of the power station are specifically as follows:
in the formula, P represents active power, Q represents reactive power, subscript i and j represent serial numbers of nodes, subscript h represents h hours, subscript g represents serial number of a power station, d represents serial number of a load, and the following is concrete:
P gi，h for the h hour, the active power of the g-th power station in the i-th node in the power grid,
P di,h for the h hour the power-consuming electric power of the d-th load in the i-th node in the grid,
P gi,min the minimum value of active power generated by the g-th power station of the ith node in the power grid,
P gi,max the maximum value of active power generated by the g-th power station of the ith node in the power grid,
Q gi,h reactive power for the g-th power station of the i-th node in the power grid at h-th hour,
Q gi,min the minimum value of reactive power generation power of the g-th power station for the i-th node in the power grid,
Q gi,max the maximum value of reactive power generation power of the g-th power station for the i-th node in the power grid,
Q di,h for the h hour the reactive power consumption of the d load in the i-th node in the grid,
V i,h for the h hour the voltage of the i-th node in the grid,
V min for the h hour the minimum value of the voltage of the i-th node in the grid,
V max for the h hour the maximum value of the voltage of the i-th node in the grid,
δ i,h for the h hour the angle of the i-th node in the grid,
G ij for the ith node and the jth node in the power gridIs used for the electric conductivity between the two,
B ij susceptance between an ith node and a jth node in the power grid;
the location marginal pricing constraint LMP is:
LMP = system reference bus marginal price-system loss component-congestion component;
in step 3, the economic evaluation comprises the evaluation of the price of the electric power spot market, wherein the evaluation method is realized through modeling, and the process is realized according to the construction of an improved GARCH model:
wherein ω, α and β are parameters of the GARCH model calculated using the maximum likelihood method, r t Logarithmic yield, σ, of power arbitrage for time t t A fluctuation rate of the electric power price at time t is represented;
then calculating to obtain the predicted standard deviation by using the electricity price return and the standard deviation in the previous time period, P T The node electricity price of the current-stage electric power is:
epsilon represents the rate of rise of the price of electricity between time periods 0 and T,the E represents the Brownian motion process, wherein the E is inverse normal distribution, the average value is 0, and the standard deviation is 1-N (0, 1);
the location marginal pricing LMP is a mechanism for managing power transmission congestion by containing market-based prices; the system additionally provides a marginal increase in cost of one megawatt LMP on the load bus k Expressed as:
where NLL denotes the number of lines of high voltage electrical connection at load limit,
μ l is the Lagrangian multiplier for line l;
LMP ref the marginal bus price is referred to for the system and is obtained through calculation of formulas (1) and (2);
α lk the sensitivity of the line flow on the line l to one megawatt change at the bus k is used for adjusting the bus to keep the balance of the system load and the generator;
the power loss of the transmission line, which is generated by increasing the load flow of one megawatt on the transmission line, is represented as a line loss sensitivity coefficient, and the coefficient is as follows:
in the formula, the unit of resistance is ohm per kilometer, the unit of line length is kilometer, the unit of power is megawatt, and the unit of Voltage is kilovolt.
2. The optimizing method of a new energy planning scheme according to claim 1, wherein in the nonlinear optimization, the objective function of the design is:
minimization (F) = (capital cost + operating fixed cost + operating variable cost-energy arbitrage revenue).
3. An optimization system for a new energy planning scheme that integrates economic and energy factors, the system comprising:
an electricity load prediction unit: the method is used for predicting the electricity load in the project effective period by using a time sequence and a machine learning mutual auxiliary method to obtain an electricity load predicted value;
nonlinear optimization unit: the method comprises the steps of carrying out nonlinear optimization processing by using a plurality of constraint conditions according to an electricity load predicted value to obtain an optimal charging scheme, optimal power and an optimal position of an energy system;
economics evaluation unit: the method comprises the steps of carrying out economic evaluation according to nonlinear optimization results and combining economic parameters and risk avoidance probability, and then obtaining an NVP expected value;
iteration unit: the method comprises the steps that the obtained NVP expected value is used as one input condition of a nonlinear optimization unit, the nonlinear optimization unit and an economic evaluation unit are started to conduct iterative processing until the maximum NVP expected value is obtained, and the optimal charging scheme, the optimal power and the optimal position of an optimal energy system corresponding to the maximum NVP expected value are used as optimization results;
the plurality of constraints include: rated energy of the energy storage system, power parameters of the power plant and location marginal pricing constraints LMP, wherein:
the rated energy of the energy storage system is specifically as follows:
wherein the method comprises the steps ofFor the rated power of the battery storage system,
η + for charging efficiency, eta - In order for the discharge efficiency to be high,
E i,h for the h hour, the i energy storage system,
E i,h-1 for the h-1 h, the i energy storage system,
storing rated energy of the system for an ith battery;
the power parameters of the power station are specifically as follows:
in the formula, P represents active power, Q represents reactive power, subscript i and j represent serial numbers of nodes, subscript h represents h hours, subscript g represents serial number of a power station, d represents serial number of a load, and the following is concrete:
P gi，h for the h hour, the active power of the g-th power station in the i-th node in the power grid,
P di,h for the h hour the power-consuming electric power of the d-th load in the i-th node in the grid,
P gi,min the minimum value of active power generated by the g-th power station of the ith node in the power grid,
P gi,max the maximum value of active power generated by the g-th power station of the ith node in the power grid,
Q gi,h reactive power for the g-th power station of the i-th node in the power grid at h-th hour,
Q gi,min the minimum value of reactive power generation power of the g-th power station for the i-th node in the power grid,
Q gi,max the maximum value of reactive power generation power of the g-th power station for the i-th node in the power grid,
Q di,h for the h hour the reactive power consumption of the d load in the i-th node in the grid,
V L,h for the h hour the voltage of the i-th node in the grid,
V min for the h hour the minimum value of the voltage of the i-th node in the grid,
V max is the firsth hours, the maximum value of the voltage at the ith node in the grid,
δ i,h for the h hour the angle of the i-th node in the grid,
G ij for the conductivity between the i-th node and the j-th node in the grid,
B ij susceptance between an ith node and a jth node in the power grid;
the location marginal pricing constraint LMP is:
LMP = system reference bus marginal price-system loss component-congestion component;
in step 3, the economic evaluation comprises the evaluation of the price of the electric power spot market, wherein the evaluation method is realized through modeling, and the process is realized according to the construction of an improved GARCH model:
wherein ω, α and β are parameters of the GARCH model calculated using the maximum likelihood method, r t Logarithmic yield, σ, of power arbitrage for time t t A fluctuation rate of the electric power price at time t is represented;
then calculating to obtain the predicted standard deviation by using the electricity price return and the standard deviation in the previous time period, P T The node electricity price of the current-stage electric power is:
epsilon represents the rate of rise of the price of electricity between time periods 0 and T,the E represents the Brownian motion process, wherein the E is inverse normal distribution, the average value is 0, and the standard deviation is 1-N (0, 1);
the location-based marginal pricing LMP is a system that includes market-based pricing to manage inputA mechanism of electrical congestion; the system additionally provides a marginal increase in cost of one megawatt LMP on the load bus k Expressed as:
where NLL denotes the number of lines of high voltage electrical connection at load limit,
μ l is the Lagrangian multiplier for line l;
LMP ref the marginal bus price is referred to for the system and is obtained through calculation of formulas (1) and (2);
α lk the sensitivity of the line flow on the line l to one megawatt change at the bus k is used for adjusting the bus to keep the balance of the system load and the generator;
the power loss of the transmission line, which is generated by increasing the load flow of one megawatt on the transmission line, is represented as a line loss sensitivity coefficient, and the coefficient is as follows:
in the formula, the unit of resistance is ohm per kilometer, the unit of line length is kilometer, the unit of power is megawatt, and the unit of Voltage is kilovolt.
4. A computer storage medium storing a computer program, characterized in that the storage medium, when run by a computer, performs the method of optimizing a new energy planning scheme according to any one of claims 1-2.
5. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when running the computer program stored in the memory, performs the method of optimizing the new energy planning scheme according to any one of claims 1-2.
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